In this paper, we propose and experiment with techniques for extreme compression of neural natural language understanding (NLU) models, making them suitable for execution on resource-constrained devices. We propose a task-aware, end-to-end compression approach that performs word-embedding compression jointly with NLU task learning. We show our results on a large-scale, commercial NLU system trained on a varied set of intents with huge vocabulary sizes. Our approach outperforms a range of baselines and achieves a compression rate of 97.4% with less than 3.7% degradation in predictive performance. Our analysis indicates that the signal from the downstream task is important for effective compression with minimal degradation in performance.

Rapid advancements in the E-commerce sector over the last few decades have led to an imminent need for personalised, efficient and dynamic recommendation systems. To sufficiently cater to this need, we propose a novel method for generating top-k recommendations by creating an ensemble of clustering with reinforcement learning. We have incorporated DB Scan clustering to tackle vast item space, hence in-creasing the efficiency multi-fold. Moreover, by using deep contextual reinforcement learning, our proposed work leverages the user features to its full potential. With partial updates and batch updates, the model learns user patterns continuously. The Duelling Bandit based exploration provides robust exploration as compared to the state-of-art strategies due to its adaptive nature. Detailed experiments conducted on a public dataset verify our claims about the efficiency of our technique as com-pared to existing techniques.

Most of the existing medicine recommendation systems that are mainly based on electronic medical records (EMRs) are significantly assisting doctors to make better clinical decisions benefiting both patients and caregivers. Even though the growth of EMRs is at a lighting fast speed in the era of big data, content limitations in EMRs restrain the existed recommendation systems to reflect relevant medical facts, such as drug-drug interactions. Many medical knowledge graphs that contain drug-related information, such as DrugBank, may give hope for the recommendation systems. However, the direct use of these knowledge graphs in the systems suffers from robustness caused by the incompleteness of the graphs. To address these challenges, we stand on recent advances in graph embedding learning techniques and propose a novel framework, called Safe Medicine Recommendation (SMR), in this paper. Specifically, SMR first constructs a high-quality heterogeneous graph by bridging EMRs (MIMIC-III) and medical knowledge graphs (ICD-9 ontology and DrugBank). Then, SMR jointly embeds diseases, medicines, patients, and their corresponding relations into a shared lower dimensional space. Finally, SMR uses the embeddings to decompose the medicine recommendation into a link prediction process while considering the patient's diagnoses and adverse drug reactions. To our best knowledge, SMR is the first to learn embeddings of a patient-disease-medicine graph for medicine recommendation in the world. Extensive experiments on real datasets are conducted to evaluate the effectiveness of proposed framework.

Learning to control the structure of sentences is a challenging problem in text generation. Existing work either relies on simple deterministic approaches or RL-based hard structures. We explore the use of structured variational autoencoders to infer latent templates for sentence generation using a soft, continuous relaxation in order to utilize reparameterization for training. Specifically, we propose a Gumbel-CRF, a continuous relaxation of the CRF sampling algorithm using a relaxed Forward-Filtering Backward-Sampling (FFBS) approach. As a reparameterized gradient estimator, the Gumbel-CRF gives more stable gradients than score-function based estimators. As a structured inference network, we show that it learns interpretable templates during training, which allows us to control the decoder during testing. We demonstrate the effectiveness of our methods with experiments on data-to-text generation and unsupervised paraphrase generation.

Nowadays, people prefer using smart and interactive apps instead of basic ones. With continually evolving technologies, it is essential always to stay up-to-date. This means that the apps, games, and gadgets must change and become more dynamic. Are you wondering how all these interactive apps of the future are created? Would you like to know how come the Google Assistant understands what you're saying and can even help you with using your phone more proficiently?

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Do you know very much about Artificial Intelligence (AI) marketing? Well lucky for you, you have come to the right place. By the end of this article, you'll learn how AI works and how you can apply it to your marketing strategies to grow your home builder business. For starters, AI is everywhere and not just the villain in those thrilling sci-fi movies you watch with your family and friends. AI is your Netflix or Amazon suggestions.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A Florida woman was arrested for allegedly setting up a dating profile advertising "free meth tonight" that sent suitors to her romantic rival's home looking for sex, police said. Vanessa Marie Huckaba, 29, created the "Islandbabe1234" profile on the website Seeking Arrangement and included the name, photo, cellphone number and address of a woman who was dating her ex-boyfriend, according to an arrest report obtained by The Miami Herald. FLORIDA WOMAN CHARGED IN MACHETE ATTACK WANTED TO BE WITH VICTIM'S WIFE: DEPUTIES "Multiple strangers began arriving at the victim's residence thereafter," said Adam Linhardt, a spokesman for the Monroe County Sheriff's Office.

Machine Learning (ML) is a well-known innovation that nearly everyone knows about. A study uncovers that 77% of devices that we presently use are utilizing ML. From a social event of SMART devices over Netflix proposition through products like Amazon's Alexa, and Google Home, artificial intelligence services are proclaiming cutting-edge innovative solutions for organizations and regular day to day existences. The year 2021 is ready to observe some significant ML and AI trends that would maybe reshape our economic, social, and industrial workings. As of now, the AI-ML industry is developing at a quick rate and gives sufficient advancement scope to companies to bring the vital change. According to Gartner, around 37% of all companies reviewed are utilizing some type of ML in their business and it is anticipated that around 80% of modern advances will be founded on AI and ML by 2022.

Applications like recommendation engines only need to be accurate enough to be somewhat useful. After all, no one is going to lose their life if Netflix recommends the wrong movie or Siri misunderstands a word. It's a completely different matter for computer vision applications that are trained to assist in critical business functions and areas like healthcare or self-driving cars. For these applications, data quality and accuracy are critical.